In spite of the mixed evidence within the clubs, the demographic approach helps us to evaluate the future growth di¤erentials between the clubs. To illustrate, consider the club-speci…c average incomes at the beginning and end of the period 1960-2000 and compare Clubs I and III, for example. Because of the considerable di¤erences in the growth rates (0:74% versus 2:49%), the income gap increased from …ve to tenfold, this increase being exacerbated by a massive demographic expansion in the former club (Figure 2). By contrast, the income gap between Clubs III and II decreased from four to 2.5 fold due to rapid growth (3:21%) in the latter club.
The theory of the demographic transition presupposes that countries move ahead to more mature stages, i.e., the demographic clubs are transitional rather than permanent, and multiple steady states may not be present in the data (Galor and Weil 2000, Galor 2007). Unfortunately, none of the techniques above can identify the data generating processes, but the dynamics of the demographic variables can give some hints. Figure 3 shows that life expectancy has increased everywhere such that its average value in Club I (Club II) in 2003 exceeded that in Club II (Club III) in 1960. Analogous information is given by total fertility, infant mortality, and
Durlauf and Johnson (1995), as it is a sub-set of our Club III. While they …nd that club 4 exhibited unconditional -convergence at 1960-1985, we …nd that it also shows both - convergence and the convergence in the …rst generation unit root tests but no convergence in the second generation unit root test at 1960-2003.
Figure 2: The per capita GDP in 1960 (grey bar) and 2003 (black bar). Population ratios indicated above the bars.
the by the dependence rate, all of which have greatly decreased since 1960 as most countries have reached the higher stages, thus supporting transitional clubs.
This makes some future explorations possible. Consider a new classi…cation derived by applying the earlier boundaries of the clubs to the values of the life expectancies in 2003. This classi…cation shows that only six countries still stay in Club I and twelve in Club II, while all other countries (67) have proceeded to Club III.14 One can now predict the average incomes, say, in 2040 illustrated in Figure 4.15 A comparison of Figures 4 and 2 shows that the future does not replicate the past. On the contrary, while the average income in Club II approached that of Club III in the1960-2003 period, in the2004-2040 period it will fall behind since the inherited low incomes provide a limited basis regardless of rapid growth (3:21%).
Thus, the countries which have migrated from Club I to Club II will experience a
14The countries staying in Club I are Burundi, Cote d‘Ivoire, Guinea-Bissau, Mozambique, Malawi, and Zambia. The countries in Club II are Benin, Burkina Faso, Cameroon, Congo, Ethiopia, Guinea, Kenya, Mali, Niger, Chad, Tanzania, and Uganda.
15There are few attempts to predict the evolution of the future incomes, and to our knowledge, none in a cross-country set-up. Holz (2008), however, uses Chinese real GDP growth rates from 1978-2000 to extrapolate the future to evaluate when the size of Chinese economy would surpass that of US in absolute terms.
Figure 3: Demographic indicators in 1960 (grey bar) and 2003 (black bar).
take-o¤, but this take-o¤ is only in terms of the country’s own history and does not raise its income closer to the more advanced countries which have already proceeded too far. Therefore, the catch-up opportunity gained by the countries that arrived in Club II in the post-war period is not open to those who will arrive later. The window of opportunity has been closed.
Figure 4: The per capita GDP in 2003 (grey bar) and 2040 (black bar).
Club III may also experience changes as 29 of its 67 members will be new-comers.
While these new-comers may grow at the rate which was typical of this club in the past, the old members may meet new growth-hampering problems, such as ageing, not visible in the1960-2003 period yet. Hence, new convergence tendencies may arise within Club III. This, together with the limited perspectives from Clubs I and II, indicates bi-polarization rather than convergence of world incomes as a whole. The number of countries in Clubs I and II, however, has decreased and their proportion of population will be smaller than before.
6 Discussion
This paper explores the role of demographics in the post-war growth and conver-gence between countries. Di¤erences in the timing of the demographic transition have segmented countries into the di¤erent regimes or clubs and the simultane-ous existence of these clubs makes the concept of convergence meaningless if the existence of such clubs neglected. We evaluate the relevance of demographics by classifying countries into demographic clubs by the regression tree method. The discriminating variable turns out to be life expectancy, probably because of its role as an indicator of technical di¤usion and as a necessary condition for investment in human capital, classifying countries into three demographic clubs.
The traditional -convergence and …rst generation unit root tests o¤er support for convergence in demographic clubs but the second generation unit root test un-dermines this result so that the evidence is equivocal. This …nding is in line with the trends in the literature, where the earlier tests suggest convergence but the most recent tests show that homogenous clubs are di¢ cult to identify even among the OECD countries. The rapid progress in the …eld of panel estimation techniques may shed further light on this subject in the future. On the other hand, the demographic transition as a source of mixed evidence calls for further research e¤orts because its phase may di¤er across countries and controlling for the initial state alone may not lead to convergence clubs. Hence, a control for the heterogeneity from the di¤erent phases may be necessary, although challenging, as the phase of transition may be dictated by economic growth itself.
Continuous demographic transition elevates countries to higher clubs, providing important implications for future incomes. Unfortunately, this information gives no unequivocal support to convergence of world incomes as the income gaps have already widened to such an extent that even the take-o¤s, typical to the second stage or club, is unable to arise the incomes of the poor su¢ ciently. Thus, new economic miracles will hardly arise on a demographic basis alone. Therefore, economic policies should be targeted to help the countries which remain in the lowest clubs. The good news from the analysis here is that the ever-richer majority of countries has better opportunity to manage this task since the number of these countries is small and their population share is low.
References
Acemoglu, D., & Johnson, S. (2009). Disease and development: The e¤ect of life expectancy on economic growth. Journal of Political Economy, Forthcoming.
Azariadis, C., & Drazen, A. (1990). Threshold externalities in economic develop-ment. Quarterly Journal of Economics 105, 501–526.
Baltagi, B., & Pesaran H. (2007). Heterogeneity and cross section dependence in panel data models: theory and applications introduction. Journal of Applied Econometrics, 22(2), 229-232.
Barro, R. J., & Sala-i-Martin, X. (1992). Convergence. Journal of Political Econ-omy, 100(2), 223-251.
Baumol, W. J. (1986) Productivity, growth, convergence, and welfare: what the long-run data show? American Economic Review, 76(5), 1072-1085.
Becker, G., Philipson, T. J., & Soares, R. R. (2005). The quantity and quality of life and the evolution of world inequality. American Economic Review, 95(1), 227-291.
Ben-David, D., & Loewy, M. (1998). Free trade, growth, and convergence. Journal of Economic Growth, 3, 143-170.
Bernard, A., & Durlauf, S. (1996). Interpreting tests of the convergence hypothesis.
Journal of Econometrics, 71, 161-173.
Bloom, D., & Williamson, J. (1998). Demographic transition and economic miracles in emerging Asia. World Bank Economic Review, 12, 419-455.
Breiman, L., Friedman, J. L., Olshen, R. A. , & Stone, C. J. (1984). Classi…cation and regression trees. Belmont. CA: Wadsworth.
Breusch, T., & Pagan, A. (1980). The Langrange multiplier test and its applications to model speci…cation tests in econometrics. Review of Economic Studies, 47, 239–
253.
Chesnais, J.-C.(1992). The demographic transition: stages, patterns, and economic implication. A longitudinal study of sixty-seven countries covering the period 1720-1984. Clarendon Press, Oxford.
Choi, I. (2001). Unit root tests for panel data. Journal of International Money and Finance, 20, 249–272.
Cuñado, J., & Pérez de Garcia, F. (2006). Real convergence in Africa in the second-half of the 20th century. Journal of Economics and Business, 58, 153-167.
Durlauf, S., & Johnson, P. (1995). Multiple regimes and cross-country behaviour.
Journal of Applied Econometrics, 10, 365-384.
Evans, P. (1998). Using panel data to evaluate growth theories. International Economic Review, 39(2), 295–306.
Fiaschi, D., & Lavezzi, A. (2007). Nonlinear economic growth: some theory and cross-country evidence. Journal of Development Economics, 84(1), 271-290.
Fogel, R. W. (1994). Economic growth, population theory, and physiology: The bearing of long-term processes on the making of economic policy. American Eco-nomic Review, 84(3), 369–395.
Fogel, R. W. (2004). The escape from hunger and premature death, 1700-2100 – Europe, America, and the Third World. Cambridge, Cambridge University Press.
Galor, O., & Weil, D. (2000). Population, technology, and growth: from Malthusian stagnation to the demographic transition and beyond. American Economic Review, 90(4), 806-826.
Galor, O. (2007). Multiple Growth Regimes–Insights from Uni…ed Growth Theory.
CEPR Discussion Paper No 6427.
Goddard, J. & Wilson, J. (2001). Cross sectional and panel estimation of conver-gence. Economics Letters, 70(3),327-333.
Hansen, B. E. (2000). Sample splitting and threshold estimation. Econometrica, 68, 575-603.
Hineline, D. R. (2008). Parameter heterogeneity in growth regressions. Economics Letters, 101(2), 126-129.
Heston, A., Summers, R., & Aten, B. (2006). The Penn World Table (Mark 6.2).
Center for International Comparisons of Production, Income, and Prices at the University of Pennsylvania.
Holz, C. (2008). China’s economic growth 1978-2025: What we know today about China’s economic growth tomorrow. World Development 36(10), 1665-1691.
Im, K., Pesaran, M., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115, 53-74.
Lee, K., Pesaran, H., & Smith, R. (1997). Growth and convergence in a multi-country empirical stochastic Solow model. Journal of Applied Econometrics, 12(4), 357-392.
Lehmijoki, U. (2009). Global Trends in Life-Expectancy: A Club Approach. Finnish Yearbook of Population Research XLIII 2007-2008, 33–51.
Levin, A., Lin, C.-F., & Chu, C.-S. (2002). Unit root tests in panel data: asymptotic and …nite-sample properties. Journal of Econometrics, 108, 1-24.
Li, Q., Papell D. (1999). Convergence of international output time series: evidence for 16 OECD countries. International Review of Economics and Finance, 8, 267-280.
Lorenzen, P., McMillan, J., & Wacziarg, R. (2008). Death and development. Jour-nal of Economic Growth, 13, 81-124.
Maddala, G., & Wu, S. (1999). A comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics and Statistics, Special Issue, 61, 631-652.
Maddison, A. (1994). Explaining the economic performance of nations, 1820-1989.
In: Baumol W, Nelson R, Wol¤ E (Eds), Convergence of productivity: cross-national studies and historical evidence. Oxford University Press: Oxford, Ch 2.
Mankiw, G., Romer, D., & Weil D. (1992). A contribution to the empirics of economic growth. Quarterly Journal of Economics, 107(2), 407-437.
Pedroni, P. (2007). Social capital, barriers to production and capital shares: Impli-cations for the importance of parameter heterogeneity from a nonstationary panel approach.Journal of Applied Econometrics, 22(2), 429-451.
Pesaran, M. (2007a). A Simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265–312.
Pesaran, M.H (2007b). A pair-wise approach to testing for output and growth convergence. Journal of Econometrics, 138, 312-355.
Pesaran, M., Ullah, A., & Yamagata, T. (2007). A bias-adjusted LM test of error cross section independence. The Econometrics Journal, 11(1), 105–127.
Ram, R. (1998). Forty years of the life span revolution: An exploration of the roles of ”Convergence," income, and policy. Economic Development and Cultural Change 46(4), 849–857.
Sala-i-Martin, X. (1996). The classical approach to convergence analysis. Economic Journal, 106, 1019-1036.
Soares, R. (2005). Mortality reductions, educational attainment, and fertility choice.
American Economic Review, 95(3), 580-601.
Soares, R. 2007. On the determinants of mortality reductions in the developing world, NBER Working Paper No. W12837.
Strazicich, M., Lee, J., & Day E. (2004). Are incomes converging among OECD countries? Time series evidence with two structural breaks. Journal of Macroeco-nomics, 26, 131-145.
United Nations. World population prospects. The 2006 revision. New York 2007.
Weil, D. (2007). Accounting for the e¤ect of health on economic growth. The Quarterly Journal of Economics, 122(3), 1265-1306.